{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FrontalDisplacement.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPfwUzZyZgGu7VLC1g48UkF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/FrontalDisplacement.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEtYJyprVeOH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVwlVemZVofg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Saturations 0-1\n",
        "sw = np.linspace(0.01,1,num=50)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqaG3AjgVvwk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "so = 1-sw"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pgrBac0rVzw9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "krw = sw**3"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2Y_GFgRV1wZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kro = (1-sw)**3"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPpZdodVV5Om",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "e678df48-de46-4d98-f54e-f09f077717ec"
      },
      "source": [
        "uo = input(\"\\nenter oil viscocity!: \")\n",
        "uo = int(uo)\n",
        "uw = input(\"\\nenter water viscocity!: \")\n",
        "uw = int(uw)\n",
        "fw = 1/(1+(uw*kro)/(uo*krw))"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "enter oil viscocity!: 300\n",
            "\n",
            "enter water viscocity!: 10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cF80odudWEXO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({'sw':sw,'fw':fw})"
      ],
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5qxWz4PWLAZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df\n",
        "qi = 1000 #bbl/d\n",
        "\n",
        "# t1 = 10 #days\n",
        "\n",
        "# x = (qi.t/A.phi)(df/dsw)\n",
        "\n",
        "A = 1000*50\n",
        "\n",
        "L = 2000 \n",
        "\n",
        "phi = 0.25\n"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54_YKJk_Wj-W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "slope = []\n",
        "for i in range(len(df)):\n",
        "  if i == 0:\n",
        "    m = fw[i]/sw[i]\n",
        "    slope.append(m)\n",
        "  else:\n",
        "    m = (fw[i] - fw[i-1])/(sw[i]-sw[i-1])\n",
        "    slope.append(m)\n",
        "\n"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMYDCHqqYgIq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# len(slope)\n",
        "# len(df)\n",
        "df['dfw/dsw'] = pd.Series(slope)"
      ],
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvvoqx8GZ-fG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "b1e93712-620a-4100-965e-14c48cfcd61a"
      },
      "source": [
        "sns.lineplot(x='sw',y='dfw/dsw',data=df)\n",
        "plt.grid()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCdgW320a3Gi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "slope = np.array(slope)"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jbsfErweaQIg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_sw_1 = (qi*t1)*5.615*slope/(A*L*phi)"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brEVRy5Qdp-Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "02e81756-c970-4514-b8d3-efe74392b235"
      },
      "source": [
        "sns.scatterplot(x = x_sw_1,y=sw)"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17d70c3780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7Aslldbg1lu",
        "colab_type": "text"
      },
      "source": [
        "#This means we need to apply welge's correction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmuXGh_2dx50",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c6ceb2fe-5d28-4377-e2e8-41731067f1a8"
      },
      "source": [
        "print(max(slope))\n",
        "print(sw[slope == max(slope)])"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.194335454868697\n",
            "[0.2322449]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LqtgU87LjZyJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c36b11d0-4cdb-422d-e65e-3cf86fecf28a"
      },
      "source": [
        "np.where(slope == 4.194335454868697)"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([11]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFGB8q09eIqG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# slope[slope<=3.511931291432257] = 3.511931291432257\n",
        "slope[:11] = 4.194335454868697\n"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unP6rvOLezaP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# slope"
      ],
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvEVz20HfRmY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "ec617d15-af36-4f4a-dddf-51e593f35d1b"
      },
      "source": [
        "sns.lineplot(sw,slope)\n",
        "plt.xlabel('Sw')\n",
        "plt.ylabel('slope')\n",
        "plt.grid()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7L3A_FNthShx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "qi = 1000 #bbl/d\n",
        "\n",
        "t1 = 10\n",
        "x_sw_1 = (qi*t1)*5.615*slope/(A*phi)\n",
        "\n",
        "t2 = 100 #days\n",
        "x_sw_2 = (qi*t2)*5.615*slope/(A*phi)\n",
        "\n",
        "\n",
        "t3 = 365 #days == 1 year \n",
        "x_sw_3 = (qi*t3)*5.615*slope/(A*phi)\n",
        "\n",
        "\n",
        "t4 = 1000 #days == 1 year \n",
        "x_sw_4 = (qi*t4)*5.615*slope/(A*phi)\n",
        "\n",
        "t5 = 6000 #days == 1 year \n",
        "# x_sw_5 = (qi*t5)*5.615*slope/(A*L*phi)\n"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0EavOU9hwvW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "3c7fc171-b7b3-4d06-99ce-f4ec596302ef"
      },
      "source": [
        "plt.figure(figsize=(10,4))\n",
        "sns.lineplot(x_sw_1,sw,label='t = 10 days')\n",
        "sns.lineplot(x_sw_2,sw,label='t = 100 days')\n",
        "sns.lineplot(x_sw_3,sw,label='t = 365 days')\n",
        "sns.lineplot(x_sw_4,sw,label='t = 1000 days')\n",
        "# sns.lineplot(x_sw_5,sw,label='t = 6000 days')\n",
        "\n",
        "plt.title('Water Front advancement with 10 cp water')\n",
        "plt.xlabel('x (ft)')\n",
        "plt.ylabel('sw or (1-so) | fraction')\n",
        "plt.xlim(0,1000)\n",
        "plt.grid()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTn3nhWhk0f3",
        "colab_type": "text"
      },
      "source": [
        "#Case 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsUgf3KUm7HC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "e65fea90-0bb6-4418-ce97-5ac9f753d9de"
      },
      "source": [
        "uo = input(\"\\nenter oil viscocity!: \")\n",
        "uo = int(uo)\n",
        "uw = input(\"\\nenter water viscocity!: \")\n",
        "uw = int(uw)\n",
        "fw = 1/(1+(uw*kro)/(uo*krw))"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "enter oil viscocity!: 300\n",
            "\n",
            "enter water viscocity!: 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MoDm9poiCm0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# uo = input(\"\\nenter oil viscocity!: \")\n",
        "# uo = int(uo)\n",
        "# uw = input(\"\\nenter water viscocity!: \")\n",
        "# uw = int(uw)\n",
        "# fw = 1/(1+(uw*kro)/(uo*krw))\n",
        "\n",
        "A = 1000*50\n",
        "\n",
        "L = 2000 \n",
        "\n",
        "phi = 0.25\n",
        "\n",
        "slope1 = []\n",
        "for i in range(len(df)):\n",
        "  if i == 0:\n",
        "    m = fw[i]/sw[i]\n",
        "    slope1.append(m)\n",
        "  else:\n",
        "    m = (fw[i] - fw[i-1])/(sw[i]-sw[i-1])\n",
        "    slope1.append(m)"
      ],
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhhHf0gsovzA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ns0iIrnnRU5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "slope1 = np.array(slope1)"
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awbd7E5anX3L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c20614be-a637-4e5f-f789-6daf78b1fce6"
      },
      "source": [
        "max(slope1)"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.904402737027941"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TcWaAv12nb8j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# np.where(slope1==max(slope1))\n",
        "slope1[:6] = 6.904402737027941"
      ],
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d11sMtcuoFJb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "8d758e4b-91d5-4cbf-d29c-cba8a5a9169c"
      },
      "source": [
        "plt.plot(sw,slope1) #applying welge's correction successfully"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f17d70100b8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NfBFA0TPlQAs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# np.where(slope1==max(slope1))"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wn-5zvmClXCT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# "
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lg6YX-htl7ro",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mAzgTVamGBN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "qi = 1000 #bbl/d\n",
        "\n",
        "x_sw_1 = (qi*t1)*5.615*slope1/(A*phi)\n",
        "\n",
        "t2 = 100 #days\n",
        "x_sw_2 = (qi*t2)*5.615*slope1/(A*phi)\n",
        "\n",
        "\n",
        "t3 = 365 #days == 1 year \n",
        "x_sw_3 = (qi*t3)*5.615*slope1/(A*phi)\n",
        "\n",
        "\n",
        "t4 = 1000 #days == 1 year \n",
        "x_sw_4 = (qi*t4)*5.615*slope1/(A*phi)\n",
        "\n",
        "t5 = 6000 #days == 1 year \n",
        "x_sw_5 = (qi*t5)*5.615*slope1/(A*phi)\n"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmkS5zDImgMd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "9f755fa2-160b-4346-ea78-42f3d1e04f84"
      },
      "source": [
        "plt.figure(figsize=(10,4))\n",
        "sns.lineplot(x_sw_1,sw,label='t = 10 days')\n",
        "sns.lineplot(x_sw_2,sw,label='t = 100 days')\n",
        "sns.lineplot(x_sw_3,sw,label='t = 365 days')\n",
        "# sns.lineplot(x_sw_4,sw,label='t = 1000 days')\n",
        "# sns.lineplot(x_sw_5,sw,label='t = 6000 days')\n",
        "\n",
        "plt.title('Water Front advancement with 1 cp water')\n",
        "plt.xlabel('x (ft)')\n",
        "plt.ylabel('sw or (1-so) | fraction')\n",
        "plt.xlim(0,1000)\n",
        "plt.grid()"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rfckjgImqLP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#You can see that, with 1000 bbl/d injection rate, it if the well spacing is around 1000 ft, it almost\n",
        "#takes around 1 year to break through. "
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mC6qUs8_veXP",
        "colab_type": "text"
      },
      "source": [
        "Conclusion : Lesser the water viscosity, quicker the breakthrough."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzTsDI2Xp_Pc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plt.style.available"
      ],
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xP8E68EaqTck",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Can ignore\n",
        "# plt.figure(figsize=(10,4))\n",
        "# plt.grid()\n",
        "# plt.style.use('default')\n",
        "# sns.lineplot(x_sw_1,sw,label='t = 10 days')\n",
        "# sns.lineplot(x_sw_2,sw,label='t = 100 days')\n",
        "# sns.lineplot(x_sw_3,sw,label='t = 365 days')\n",
        "# # sns.lineplot(x_sw_4,sw,label='t = 1000 days')\n",
        "# # sns.lineplot(x_sw_5,sw,label='t = 6000 days')\n",
        "\n",
        "# plt.title('Water Front advancement with 1 cp water')\n",
        "# plt.xlabel('x (ft)')\n",
        "# # plt.ylabel('sw or (1-so) | fraction')"
      ],
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTy-DTozq3kp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}